From AlphaGo vs. Ke Jie, to the OpenAI bloody Dota2 semi-professional player, AI once again took to the stage of history. Since 2012, artificial intelligence has begun to break out due to the increase in the amount of data and the increase in the computing power of deep learning.

However, just in March of this year, Caijing published an article "China's AI company suffered a C-round death" caused a sudden loss; in May there were reports that IBM's medical department was significantly laid off, the scale reached 50%-70%; then the US medical media STAT It is news that STAT got the file from Andrew Norden, then the deputy chief health officer of IBM Watson Health, which showed that doctors who are using Watson for Oncology have strongly criticized Watson often. Making inaccurate medical advice has put IBM Watson in the biggest crisis of trust in history. Recently, more media research pointed out that many medical imaging AI products lie in the hospital "eat ash."

Behind the medical AI carnival is the embarrassment that new technologies frequently encounter landing scene applications. Is medical AI a concept of obscenity or just a capital game? If the technical scene can be achieved, what characteristics of the product can be truly accepted by the hospital, to avoid the fate of "eat gray"?

At present, the development trend of artificial intelligence is unstoppable. From abroad to China, from capital giants to technology giants, the smart medical industry has been actively deployed. Medical imaging AI is highly favored by capital and is considered to be the first to be commercialized, and it is expected to achieve overtaking in corners.

At present, many companies have developed products that assist doctors in different departments and are accelerating business layout. However, after the carnival from 2017 to the first half of 2018, medical AI companies did not seem to hand over a beautiful answer. It is reported that the medical AI company that cooperates with the imaging department of Zhejiang Shaoyifu Hospital has reached 10 cases.

According to Chai Xiangfei, CEO of Huiyi Huiying, AI+ medical entrepreneurship is still in its infancy, and there is still much room for improvement in the underlying technology, product creativity and product experience. At present, medical AI products lie in the hospital "eat ash" for several reasons: First, everyone has a relatively high expectation for medical AI, and the current AI medical requirements for speed and the current slow change in the medical industry is now the biggest Contradictions, the overall development cycle is longer. While medical care is a traditional industry, there are many characteristics of the original industry. For example, it may take 10 to 15 years for new drugs to be developed and commercialized, and the research and development of devices is 5 to 10 years.

Second, most of the products are actually being tested in hospitals. The ease of use and friendliness are still to be investigated. At present, AI is too shallow to participate in clinical practice. We believe that AI companies should not only design products with doctors as the center. The product is integrated into the doctor's work scene, and also meets the doctor's usage habits to help doctors improve the diagnostic efficiency and accuracy; in addition, we also need to design the product centered on the patient, and truly serve the patient and improve the patient's The medical experience is the end of product design. Creating easy-to-use and friendly medical AI products is the key to success. For example, Huiyi Huiying provides intelligent report interpretation services to patients on the basis of existing business digital films, which has been welcomed by hospitals and patients.

Differentiated competition, breaking through the ceiling

AI technology can benefit from data training and master different professional knowledge. Artificial intelligence medical imaging is the use of image recognition methods to determine the picture. There may be several types of data in medicine, mainly image data and pathological data. Many methods can be solved by image recognition.

Pulmonary nodule screening is an area where most AI imaging companies are currently concentrated. Although AI can help identify nodules, AI has not been able to draw conclusions on further benign and malignant judgments and reports. Moreover, most of the products on the market are concentrated on the pulmonary nodules, and the homogenization is serious. A top three hospital may install more than 10 AI products at the same time, but one or two of them are often used. Other products are optimized because there is no doctor's feedback. The product is iteratively slow and appears to be lying in the hospital. Happening.

There are many public data on lung nodules, and many data sets can be downloaded directly. Therefore, in the past two years, a large number of companies have developed lung nodule screening products. However, for a wider range of diseases, the development of AI products has been difficult. It is difficult to obtain new disease data, and high-quality data requires experts to co-label, and the development cycle of the entire disease AI product is long. In addition, the unique identification of images in medical settings is not enough to meet the needs of doctors. The clinical value of disease screening and assisted diagnosis is limited. To become an indispensable tool for daily use of doctors, it is necessary to intervene in clinical decision-making, doctors. There is a need for AI products that cover all medical processes.

Therefore, products that penetrate more diseases and participate in more medical processes may be supported and affirmed by more hospitals and doctors, which may be the most important competitive advantage of AI Imaging Company in the future. At this point, Huiyi Huiying has a unique approach, hoping to cut through the image data, let AI through the entire process of imaging, to achieve a service closed loop from screening to diagnosis to treatment and prognosis.

In 2017, Huiyi Huiying released three more commonly used graphic images for screening scenes, such as CT lung nodule detection, chest DR detection and fracture detection. The training model is not only in the training model. The information of the image, together with the clinical information of many patients, the information of the test and the information of the follow-up, the AI ​​products can not only achieve the location and labeling of the lesions, but also participate in the staging of the tumor and can treat the doctor. Decision support.

In April 2018, Huiyi Huiying and 301 Hospital released the aortic sandwich artificial intelligence platform AORTIST2.0, which combined the development of new disease model with the whole process coverage of single disease. Through verification, the accuracy of AORITST2.0 far exceeds that of conventional manual measurements, and it also provides prognostic predictions for aortic dilation and composite endpoint events. The performance of AORTIST2.0 can basically reach the accurate judgment and prediction level of 301 hospitals, which can reduce the 40% of the five years to 15%, and can further deepen the medical scene and decision-making process.

Hui Yihua CEO Chai Xiangfei said that medical AI has entered the second half, AI has moved from the 1.0 era to the 2.0 era. The reason for this change is that in the past 1-2 years, we have focused on improving the diagnostic efficiency for doctors. In an effort, today we find that the real core of medical services is patients, patient-centered, and it is vital to get through the service chain from patients to doctors to hospitals.

Huiyi Huiying is committed to creating an online imaging center that connects patients, hospitals and doctors to provide a digital, mobile and intelligent closed-loop image service system for the three, and tries to build a platform that can continuously incubate innovative medical services. Develop in depth and try to participate in medical treatment and prognosis follow-up. At the same time, the most distressed profit dilemma in the industry was the result of the full-chain service business model to break the deadlock, and to break out the curse of burning money. It is reported that Huihui Huiying has a beautiful income transcript in 2017. It has been regarded as a quasi-unicorn and a unicorn by the industry.

China's medical field faces two problems. One is the lack of medical resources, and the other is the uneven distribution of medical resources. According to the China Health and Family Planning Statistical Yearbook data, among all medical institutions in China, there are 28,261 urban hospitals, the number of primary medical institutions is 927,147, and the primary medical institutions are 32 times that of urban hospitals.

Statistics on the number of various medical institutions in China in 2016

According to a misdiagnosed data from the Chinese Medical Association, the total misdiagnosis rate of clinical medical care in China is 27.8%, and the average misdiagnosis rate of malignant tumors is 40%. These misdiagnosis mainly occurs in primary medical institutions. The total number of misdiagnosed cases in China exceeds 5,700 each year, which is four times that of the United States.

Huihua Huiying co-founder and COO Guo Na said: The scarcity and scarcity of quality medical resources is a fundamental contradiction. What is really lacking in primary hospitals is the diagnostic ability, and the diagnostic ability is a scarce resource. As an inevitable starting point for grading diagnosis and treatment, artificial intelligence can enable grassroots people to enjoy homogenized resources and services.

In this regard, Huiyi Huiying hopes to use the medical association as a carrier to open up online and offline, providing a complete set of services for primary hospitals. Taking the Third Affiliated Hospital of Zhengzhou University as an example, Huihui Huiying uses cloud computing, big data, Based on artificial intelligence technology, it will create a complete set of women's and children's alliance medical intelligence imaging center to realize medical image information and diagnostic information sharing in the medical association, promote the integration and sharing of medical imaging resources and imaging medical resources, and the women and children in the medical association. Medical institutions can realize the uploading and centralized management of image data, as well as the sharing and review of image information and diagnostic reports, and finally achieve the sinking of high-quality medical resources.

The Third Affiliated Hospital of Zhengzhou University is not a case. It is reported that there are dozens of such cases in the Medical Imaging Center. Guo Na said that in order to further promote the popularization of medical AI in primary medical institutions and greatly improve their diagnosis and treatment capabilities, Huihui Huiying will launch 100 plans for AI-enhanced grading medical treatment, first providing 100 primary hospitals for a period of 1 Free screening for lung nodules for the year. At present, Huifei Huiying's lung nodule screening products can quickly achieve the location and labeling of lesions. The automatic recognition accuracy of CT lung nodules exceeds 90%, which helps doctors improve the diagnostic efficiency and reduce the rate of missed diagnosis and misdiagnosis.

Referring to the screening of lung nodules, Guo Na said that this is based on the current national conditions and future strategic goals. On the one hand, lung cancer can be said to be the number one killer of cancer in China. According to the 2013 China Cancer Registration Annual Report issued by the National Cancer Registry, the annual incidence of lung cancer is about 600,000. Lung cancer accounts for 20.48% of the incidence of all malignant tumors in the city, and the mortality rate is 27.05%; it accounts for 18.05% of the incidence of malignant tumors in rural areas, 22.42% of the mortality rate, and the morbidity and mortality are on the rise. . Pulmonary nodule screening can help primary hospitals provide primary screening services.

On the other hand, the state is vigorously pursuing a grading diagnosis and treatment policy, and the medical imaging cloud platform provided by Huihui Huiying can provide a good cloud platform support for medical associations and grading medical treatment, and help data between primary medical institutions and urban hospitals. Sharing with doctor resources, truly achieving "basic inspection, superior diagnosis", can be said to respond to the national grading diagnosis and treatment call.

As Chai Xiangfei, CEO of Huiyi Huiying, said, “In the context of insufficient overall medical capacity, medical artificial intelligence is applied to the grassroots level not only at the current level, but also in a relatively mature environment. First, using artificial intelligence to help doctors improve the efficiency of diagnosis, doctors will be Emancipated from inefficient repetitive labor; Second, help grassroots doctors improve their diagnostic level and reduce the rate of misdiagnosis at the grassroots level. Although this road is difficult to walk, we can't do it because it is difficult to do it. Nothing does not have any value."

At present, Huiyi Huiying AI and image cloud platform products have entered nearly 800 hospitals and are widely used in hundreds of medical institutions. It can be said that the AI+ primary medical institution is an excellent "CP", which realizes the win-win situation of AI medical enterprises, primary medical institutions and grassroots patients, and has a great impetus to the development of China's AI medical industry.

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